physics-discriminate-model-families-and-defend-the-fit

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Use at analysis when a fit is about to be reported as the answer without a rival model being excluded. Covers naming the competing model families, showing which the data rules out, and treating the fit protocol — range, weighting, priors — as part of the result rather than as a setting.

AI & Automation 804 stars 25 forks Updated today NOASSERTION

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# Physics: exclude a named model, and treat the fit protocol as part of the result Name the competing model families before you touch the data. A physics result is not "our number is good"; it is "quantity Q, measured against control X with everything else fixed and carrying an uncertainty, follows family A and excludes family B". Fit every candidate family to the same data on a common footing, report a per-family goodness-of-fit, and state which families are excluded and at what confidence. The supplied materials usually name the alternatives; where they do not, the conventional theory and a trivial null are still required arms. When the reported number is a parameter of a functional form, an exponent, a slope, a critical value, treat the fit protocol as part of the result. That value depends on the abscissa, the fit window, the weighting, and which reference quantities (amplitude, critical point, offset) are held fixed rather than free. Enumerate those choices, report the parameter under the field's standard convention first, and attach a sensitivity table over the alternatives. Test the fixed-exponent model with a goodness-of-fit statistic in addition to free-fitting the exponent: the two answer different questions and can disagree by a factor of two when the small-signal end is noise-dominated. Run the original protocol on its own terms and report that outcome as the headline for each target result. Data-provenance problems and alternative interpretations belong in a s...

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Author
tangxiangru
Repository
tangxiangru/AutoR
Created
5 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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